Module manager: Dr Sandra Lancheros Torres
Email: S.P.LancherosTorres@leeds.ac.uk
Taught: Semester 2 (Jan to Jun) View Timetable
Year running 2019/20
LUBS1280 - Mathematical Economics OR LUBS1260 Mathematics for Economics and Business 1 OR both MATH1710 - Probability and Statistics I and MATH1712 - Probability and Statistics II
| LUBS2225 | Credit and Financial Analytics |
| LUBS2925 | Modelling Techniques for Business Analytics |
This module is not approved as a discovery module
This module provides you with an introductory knowledge of applied econometric techniques and relevant software. The module introduces the basic assumptions and interpretation of the linear regression with one regressor. It extends this model to incorporate additional regressors in the multivariate regression analysis. Additionally this module assesses the particular problems that may arise in regression analysis such as, multicollinearity, autocorrelation, heteroskedasticity and omitted variable bias.
The aim of this module is to introduce students to the basic tools of econometrics to enable them to use these techniques to test economic theory It also provides the basic explanation of the analysis of modern time series economic data.
Upon completion of this module students will be able to:
- Explain and identify basic applied econometric techniques, and econometric theories and methodologies
- Interpret the outcomes of econometric analysis
- Assess the reliability of the results from a regression analysis, namely to evaluate the external and internal validity of a regression analysis
Upon completion of this module students will be able to:
Transferable
- Apply analytical and problem solving skills
Subject specific
- Apply econometric techniques and appropriate software to economic, accounting and financial analysis
Indicative content:
- The nature of econometrics
- The basic linear regression model
- Ordinary least squares (OLS)
- Interpretation & assumptions of basic models
- Multivariate regression analysis
- Problems in regression analysis
- Multicollinearity
- Autocorrelation
- Heteroscedasticity
- Omitted variables
| Delivery type | Number | Length hours | Student hours |
|---|---|---|---|
| Computer Class | 5 | 1 | 5 |
| Lecture | 22 | 1 | 22 |
| Tutorial | 4 | 2 | 8 |
| Private study hours | 65 | ||
| Total Contact hours | 35 | ||
| Total hours (100hr per 10 credits) | 100 | ||
Progress monitoring will take place through the following routes:
- Individual feedback given in assignments for periodic seminars and computer sessions;
- Active communication using Announcements on the VLE;
- Practice exams (final exam) available via the VLE;
- Past exam papers with detailed solutions (final exam) available via the VLE;
- Access to teaching staff in weekly scheduled office hours;
| Assessment type | Notes | % of formal assessment |
|---|---|---|
| Computer Exercise | Continually assessed computer lab workshops and seminars | 30 |
| Total percentage (Assessment Coursework) | 30 | |
The resit for this module will be 100% by 1 hour examination.
| Exam type | Exam duration | % of formal assessment |
|---|---|---|
| Standard exam (closed essays, MCQs etc) | 1.0 Hrs 0 Mins | 70 |
| Total percentage (Assessment Exams) | 70 | |
The resit for this module will be 100% by 1 hour examination.
The reading list is available from the Library website
Last updated: 30/04/2019
Errors, omissions, failed links etc should be notified to the Catalogue Team